Hybrid token learning with bidirectional attention for few-shot semantic segmentation

2026-07-04

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Liting Lei

Yujie Zhang

Yadang Chen *

Jianlin Qiu

DOI：10.1007/s00371-026-04619-3

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Few-shot semantic segmentation (FSS) aims to segment objects of unseen categories using only a handful of annotated samples. A central challenge lies in learning transferable representations that generalize across large intra-class variations and high inter-class similarity under extreme data scarcity. In this work, we introduce a novel hybrid token learning framework equipped with bidirectional attention to tackle these issues. Our approach first extracts adaptive tokens from support images, encoding both target-specific details and background context, and then integrates them with a set of learnable target-agnostic tokens to form a hybrid token representation. These tokens are refined through a Symbiotic Attention Refinement Module, which employs bidirectional masked cross-attention to enable co-adaptive optimization between tokens and query features. Experiments on standard natural image benchmarks, PASCAL- $$5^{i}$$ and COCO- $$20^{i}$$ , demonstrate that our method achieves competitive performance with only 4.5M parameters. The results confirm that our hybrid token representation effectively mitigates distribution bias and enhances generalization in few-shot segmentation.

Keywords:

Few-shot learning

Semantic segmentation

Token learning

Transformer

Hybrid token learning with bidirectional attention for few-shot semantic segmentation

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